Economics of Education Review 0 0 0 (2017) 1–16

Contents lists available at ScienceDirect

Economics of Education Review

journal homepage: www.elsevier.com/locate/econedurev

Impacts of new school facility construction: An analysis of a

state-financed capital subsidy program in Ohio

✩

Michael Conlin

a , Paul N. Thompson

b , ∗

a Department of Economics, Michigan State University, 110 Marshall-Adams Hall, East Lansing, MI 48824, United States b Department of Economics, Oregon State University, 340 Bexell Hall, Corvallis, OR 97331, United States

a r t i c l e i n f o

Article history:

Received 30 August 2016

Revised 22 May 2017

Accepted 25 May 2017

Available online xxx

JEL classification:

H71

H75

I2

Keywords:

School districts

Capital expenditures

Test scores

Housing prices

a b s t r a c t

This paper analyzes Ohio’s capital subsidy program which distributed over $10B for school construction

in 231 school districts between 1997 and 2011. Using an instrumental variables estimation, we find the

percentage of students meeting test score proficiency thresholds decrease in math and reading in the first

couple years after the capital expenditures and then increase in subsequent years. These results are con-

sistent with short-term disruptions in student learning followed by long-term benefits from the capital

expenditures. We also consider mechanisms by which capital expenditures affect achievement and find

some evidence that changes in capital expenditures are correlated with changes in operating expendi-

tures, suggesting that some of these effects may be attributable to operating expenditures. We find simi-

lar effects of these capital investments on the housing market. While in the short-term these construction

projects decrease home prices, the housing market does benefit in the long-term from improvements to

the capital stock.

© 2017 Elsevier Ltd. All rights reserved.

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. Introduction

In the years since a 1995 report on school facilities indicated

hat school buildings in the United States were in severe disre-

air ( U. G. A. Office, 1995 ), just under one trillion dollars has been

pent on new school building construction and an additional $200

o $300 billon has been spent to maintain this deteriorating capital

tock ( 21st Century School Fund, 2009; Filardo, 2016 ). Despite this

arge investment, depreciating school district infrastructure contin-

es to be an ongoing policy concern in the United States. In fact, a

014 survey of school facilities found that an additional $200 bil-

ion of expenditures would be required to avoid deferred mainte-

ance and upgrade all of the United States’ public school buildings,

hich currently have an average age of 44 years, to a “good over-

ll condition.” The report also noted that school facility issues are

ost prevalent in poor school districts, where 60% of school build-

✩ The authors are grateful to the participants of the 2015 Association for Educa-

ion Finance and Policy Conference and the 2016 Western Economics Association In-

ernational Conference for their helpful comments and discussion. A special thanks

o Rick Savors and others at the OSFC for their help in collecting the OSFC project

ata and answering our many questions about the program. ∗ Corresponding author.

E-mail addresses: conlinmi@msu.edu (M. Conlin),

aul.thompson@oregonstate.edu (P.N. Thompson).

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ttp://dx.doi.org/10.1016/j.econedurev.2017.05.002

272-7757/© 2017 Elsevier Ltd. All rights reserved.

ngs need substantial upgrades to be considered in good condition

Alexander & Lewis, 2014 ).

Poor facility quality is particularly concerning given that school

uildings are one of the key inputs into the educational production

unction. Older school facilities, which often have poorer air qual-

ty ( Daisey, Angell, & Apte, 2003 ), lighting, and technological ca-

abilities ( Lyons, 1999 ), are often associated with lower test score

erformance and higher student absences, suspensions, and other

egative behaviors ( Earthman, 2002; Schneider, 2002 ). As these

chool buildings are workplace environments for teachers, there

s also evidence that older facilities may hinder the instructional

apabilities of teachers ( Lemasters, 1997 ), lead to poorer attitudes

owards teaching ( Dawson & Parker, 1998; Lowe, 1990; Schneider,

0 03; Uline & Tschannen-Moran, 20 08 ), and impact attrition deci-

ions ( Buckley, Schneider, & Shang , 2004). Beyond impacting stu-

ents and teachers directly, increases in facility quality may also

e reflected in increased housing values if residents benefit from

he increases in student outcomes or the increased safety and aes-

hetic appeal of new and renovated buildings ( Cellini, Ferreira &

othstein, 2010 ; Neilson & Zimmerman, 2014 ).

Given the potential positive effects of improving school facili-

ies, many states have instituted specific policies to encourage in-

estment in school infrastructure. We analyze one such policy in

hio, where the state provides a subsidy (i.e., a percentage of the

otal project cost) to encourage upgrades to school facilities, partic-

2 M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16

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ularly in poorer districts. Between 1997 and 2011, the Ohio School

Facilities Commission (OSFC) disbursed over $10 billion towards

the improvement of local school facilities in 231 school districts. 1

This paper analyzes the effect of this state-financed capital sub-

sidy program on student performance and housing prices using an

instrumental variables identification strategy. In particular, we use

variation in the timing of school district eligibility for the state

subsidy, as established by a wealth ranking and yearly cutoff, as

an instrument for capital expenditures and the value of the cap-

ital stock. In terms of student performance, we find decreases in

the percent of the school district that tests proficient in math and

reading in the first couple of years after the capital expenditures

and increases in these percentages in the long-term as students

benefit from the improved capital stock. This short-term decline

is consistent with the premise that construction projects are dis-

rupting student learning, due to construction noise, displacement

of students while buildings are renovated, or issues with build-

ing consolidations. Test scores improve once the construction of

new and renovated buildings is completed. This paper also consid-

ers the mechanisms through which these construction projects af-

fect district test scores. Our descriptive analysis indicates a nearly

$900 per pupil increase in operating expenditures in the first six

years of program eligibility as the capital subsidy may allow dis-

tricts to redirect general funds from capital to operating expen-

ditures, suggesting that the program may impact test scores and

housing prices through both increases in capital and operating ex-

penditures. We also find some evidence that program eligibility is

correlated with changes in student body composition – suggesting

that perhaps student enrollment decisions are influenced by im-

provements in building quality. Similar to math and reading exam

performance, we find a negative housing price effect of recent cap-

ital expenditures (i.e., within the last two years), but positive long-

term impacts of these capital investments. This negative short-

term effect on the housing market could be the result of home-

owners paying taxes towards funding these projects while the on-

going capital projects provide little immediate benefit and may be

imposing costs by disrupting the learning environment. Once con-

struction is complete, however, the positive benefits of the new

capital stock (e.g., increased academic performance; cleaner, qui-

eter, and healthier learning environment) are fully realized and the

disruption costs of construction are no longer present, leading to

positive housing price effects.

2. Previous literature

Because school district facilities are often viewed as a key in-

put into the education production function, the empirical research

investigating the relationship between capital investment, student

achievement, and housing prices is extensive. Much of the early

literature on the relationship between school building quality and

student achievement uses cross-sectional variation in school dis-

trict capital expenditures. A survey of this literature by Hanushek

(1997) notes that these studies find mixed evidence 2 on the rela-

tionship between school facilities and student performance. More

recent literature surveys and meta-analyses ( Bailey, 2009; Gunter &

Shao, 2016; Lemasters, 1997 ) have similarly found mixed evidence

1 According to the 2016 State of our Schools Report ( Filardo, 2016 ) between 1994

and 2014, state revenue accounted for $12.67 billion of the $46.4 billion of capital

outlay that was undertaken in Ohio (27%). This level of state aid ranked Ohio 4th

out of 50 states (behind only California, Massachusetts, and New York) in terms of

total dollar amount of state revenues for capital outlay and 14th out of 50 states in

terms of the percentage of capital outlay that was funded by state revenue. 2 Of the 91 studies identified as examining school facility quality and student

performance, 32% found a positive relationship, 24% found a negative relationship,

and 44% found relationships of indeterminate sign.

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n the relationship between school building quality and student

chievement.

Recent literature has focused on using quasi-experimental de-

igns to identify the causal effect of capital investment on stu-

ent outcomes and housing prices. A set of quasi-experimental pa-

ers ( Cellini et al., 2010 ; Hong & Zimmer, 2016; Kogan, Lavertu,

Peskowitz, 2017; Martorell, Stange, & McFarlin, 2016 ) estimate

egression discontinuity designs using the majority rule cutoff

n school bond referendum elections to compare outcomes (test

cores and/or housing prices) for districts that just pass a bond ref-

rendum to fund additional capital expenditures to those that just

ail to pass a bond referendum and generally find mixed evidence

n the role of capital investments on student achievement. Hong

nd Zimmer (2016) examine reading proficiency in Michigan and

nd small effects in the first four years after bond proposal, but

nd long-run effects of between 0.4 and 0.7 percentage point in-

reases in reading proficiency. Martorell et al. (2016) examine bond

lections in Texas and conclude there is little impact of school cap-

tal investments, if any, on student achievement. Kogan, Lavertu,

nd Peskowitz (2017) examine tax elections for both capital and

perating revenues and find a 0.05 to 0.1 standard deviation in-

rease in the performance index (i.e., an aggregate index of test

core performance across all tested grades) for districts that passed

tax. Although the effects are imprecisely estimated, Cellini et

l. (2010) find a 0.077 standard deviation increase in math scores

nd a 0.067 standard deviation increase in reading scores follow-

ng bond passage in California. Their study also examines the ef-

ects of bond referendum passage on housing prices and finds that

arginal homebuyers are willing to pay $1.50 for an additional

ollar of capital expenditure per pupil.

Unlike the studies using bond election outcomes to examine

he effects of upgrades to a small number of school buildings

ithin a district, Neilson and Zimmerman (2014) and Goncalves

2015) examine the effects of upgrading an entire school dis-

rict’s capital stock. Neilson and Zimmerman (2014) use difference-

n-differences and an event study model to examine the impact

f a school construction project in the New Haven (CT) Public

chool District, which increased per pupil capital expenditures by

oughly $70,0 0 0, on test scores, enrollment, and housing prices.

hey find that six years after construction of a new building, stu-

ent reading scores increase by 0.15 standard deviations, housing

rices increase by 10%, and neighborhood residency enrollments

ncrease by 17.3%, but do not find any statistically significant im-

act on math test scores. Goncalves (2015) , a concurrent analy-

is of the OSFC, uses difference-in-differences and an event study

odel to analyze the effects of program participation on school

istrict outcomes, test scores, and housing prices. Using program

articipation as the main source of identification, Gonclaves finds

hat completion of these state-subsidized projects increases en-

ollments and property values and narrowed the disparity in ex-

enditures across the wealth distribution. Similar to our analy-

is, Gonclaves finds that test scores were reduced during building

onstruction.

Although the purpose of our paper is quite similar to many

f these previous studies, our study has some notable contribu-

ions. One of the main contributions of our study is the collec-

ion and use of annual school district-level capital expenditures

nd the value of the capital stock, which allows us to separately

stimate the short-term disruption effect of on-going construction

nd the longer-term effects of improvements to the quality of the

apital stock on test scores and home prices. In addition, our em-

irical design uses the yearly ranking and cutoffs that determine

ligibility for the program as instruments for capital expenditures

nd the value of the capital stock to obtain causal estimates of the

ynamic effects of capital expenditures on district test scores and

ome prices. This empirical strategy has some advantages over the

M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16 3

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ethodological challenges present in some of the previous liter-

ture. Most notably, our instrumental variables analysis uses pro-

ram eligibility, which abstracts away from the voter decision on

hese projects. Thus, we avoid the challenges associated with ad-

ressing the selection issues associated with bond passage and

ith subsequent elections for bond referenda that failed the first

ime they are placed on the ballot. Our identification strategy is

lso substantially different from the approach taken by Gonclaves

o assess the effectiveness of the OSFC program. Although the

onclaves study uses program participation for identification, we

refer to use program eligibility because of the challenges of ad-

ressing the selection issues associated with program participation

ecisions by school districts. 3

In terms of generalizability of the results, our paper has some

lear advantages and some disadvantages compared to the pre-

ious literature. While previous work using the election regres-

ion discontinuity designs focused solely on locally-funded cap-

tal projects, our study and the Gonclaves study, may general-

ze to other states where capital projects are jointly funded by a

tate building aid program subsidy and local funding. Currently, 39

tates have a building aid program in place and 32 of those have

ome type of matching grant program similar to the one found in

hio ( Wang, 2004 ). A notable limitation of our study relative to

he bond election studies is that these previous studies are able to

raw inferences for a wide range of districts because any district is

ligible to propose a school bond. Our study can speak only to the

elatively poor districts eligible for the OSFC program. We believe

hat this is still an interesting group of districts to study, but can-

ot conclude much about the returns to capital expenditures for

elatively wealthy districts. Another limitation of our study is that

ue to data constraints we are limited to district-level proficiency

ates instead of individual student test scores. Hong and Zim-

er (2016) , Goncalves (2015) , and Kogan, Lavertu, and Peskowitz

2017) face similar data constraints in this regard and therefore the

agnitudes of our results are likely to be more directly compara-

le to the results of these studies than those found in Cellini et

l. (2010 ) or Neilson and Zimmerman (2014) , which use individual

tudent test scores. Given that the OSFC program we examine here

rovides upgrades to the district’s entire capital stock, we expect

ur housing price results to be closer to those found in Neilson

nd Zimmerman (2014) and Goncalves (2015) , which also focus

n district-wide capital stock upgrades, than those found by the

lection regression discontinuity papers for largely single building

pgrades.

3 Our main concern with using program participation for identification is the se-

ection issue associated with an eligible district’s decision to participate in the pro-

ram. The perceived costs and benefits associated with subsidized capital expen-

itures from the CFAP are likely to vary depending on the eligible district’s par-

icipation decision. While the data perhaps allow us to effectively account for per-

eived costs through the local share and projected costs information, our ability to

ccount for differences in perceived benefit is difficult due to the systematic differ-

nces across districts in their existing capital stocks, forecasted changes in student

nrollment, quality of the school administration/board, and voters’ perceptions of

hese district attributes. Voters’ perceptions are important because program par-

icipation involves actions by the district administration/board as well as, in most

ases, passage of a referendum to fund the district’s local share. These differences in

erceived benefit not only influences district participation but are also likely to be

orrelated with changes in test scores and home prices. Therefore, if identification

s based on program participation, we would have to address the selection issues

ssociated with a district’s decision to pursue participation in the CFAP as well as

he selection issues associated with the voters’ decision to pass the tax referendum.

n our opinion, the selection issues associated with the decision to participate are

ifficult to address with our data.

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. Institutional details

.1. School funding in Ohio

The 613 public school districts in Ohio are primarily funded

hrough local revenue and state aid. 4 Both income taxes and prop-

rty tax millages are used to generate local funds for operating and

apital expenditures. All districts have specific property taxes, such

s current expense and emergency operating levies, that generate

evenue for operating expenditures and some districts obtain ad-

itional operating revenue through a local income tax. The major-

ty of local funding for capital expenditures involve debt issuances

hrough bonds to fund the construction of new classroom facilities

nd improvements/renovations to existing facilities. In addition to

ebt issuances, districts can use permanent improvement levies to

und short-term, at most five year, capital improvements. These in-

ome and property tax levies are subject to voter approval, with

ax referenda placed on the ballot during one of Ohio’s uniform

lection dates. 5

Along with this local funding, the state distributes to districts

he per pupil funding necessary to provide students with an “ad-

quate” level of educational services. This state foundation aid,

hich is often used to fund operating expenditures, is redistribu-

ive, with low wealth districts receiving disproportionately more

rom the state. While some of the money provided to districts

hrough state foundation aid is used to fund maintenance of exist-

ng school buildings, 6 the state also provides subsidies for capital

nvestment through the OSFC.

.2. The Ohio School Facilities Commission

The OSFC was created in 1997 and initially provided $300 mil-

ion in funding to help rebuild Ohio school facilities. Over the past

wenty years, the commission has been funded through three main

ources of revenue: (a) cash from general revenue funds; (b) cash

rom the Master Tobacco Settlement Agreement; and (c) State of

hio General Obligation Bonds. The OSFC funds several different

chool capital initiatives, including the Urban Program, the Excep-

ional Needs Program (ENP), the 1990 Lookback Program, and the

lassroom Facilities Assistance Program (CFAP). The Urban Program

argets capital funds to the Akron, Cincinnati, Cleveland, Columbus,

ayton and Toledo city school districts while the ENP focuses on

acilities that pose a health and safety risk to students. The 1990

ookback Program is available for the 43 school districts that re-

eived capital funds from the state‘s building assistance program

hat preceded the OSFC. 7

For those districts interested in the CFAP, the OSFC first evalu-

tes their existing facilities to determine the capital needs of the

istrict. 8 The CFAP targets the poorest districts first by provid-

4 For full documentation on the school funding system in Ohio during the period

f the study, see the Ohio Legislative Service Commission “School Funding Complete

esource” ( http://www.lsc.state.oh.us/schoolfunding/edufeb2011.pdf ). 5 Districts can place tax referenda on the ballot up to three times per year during

ither the November general election, the May primary election or a special election

eld in February or August. During presidential election years there are only three

lection dates – March, August, and November. 6 Within the funding formula used during the time period of the study, districts

eceived $884 per student for operations and maintenance and $250 per student

or technology equipment ( O. L. S. Commission, 2011 ). 7 These early projects addressed some of the facility needs in these districts, but

ot all. Thus, the OSFC was given the opportunity to “look back” at these districts

o see if funds would be needed to address any remaining unmet facility needs. 8 This facility assessment report provides information on the land acreage, build-

ng capacity, number of floors, total building square footage, dates of construction

or the original building and any additions, and evaluations of the condition of 23

eparate building systems and components. To determine space needs for the new

r renovated buildings, the OSFC provides a ten-year enrollment projection based

4 M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16

ing access to state funds for almost their entire classroom facility

needs. 9 To determine which districts are eligible for CFAP funding

in a given year, districts are ranked based on measures of taxable

property value per pupil and median income. Each year a rank-

ing cutoff is established, with districts below the cutoff eligible for

funding and those above not eligible. Using this approach, the least

wealthy districts are the first to become eligible for the funding,

with wealthier districts becoming eligible for the funding in subse-

quent years – as the cutoff is moved farther up the equity ranking.

Districts that are eligible to receive CFAP funding must raise some

percentage of the total cost of the project using local funds (i.e.,

the local share). The state then provides the district with a sub-

sidy equal to the difference between the total cost of the project

and the local share. The size of the local share largely depends on

what percentile of the equity ranking the district is located. For

example, a district in the 25th percentile of the ranking must raise

local funds to pay for 25% of the project’s total cost. The number

of districts that are funded each year varies based on the size of

the state share of funding and the total amount appropriated to

the OSFC. Districts that accept CFAP funding cannot receive another

offer of state subsidized capital funding for 20 years.

3.3. Trends in CFAP eligibility and receipt

Due to differences in the size and scope of projects funded

through the Urban program compared to those in the CFAP 10 and

restrictions on the number of districts eligible each year for the

Lookback program, 11 our study focuses on the 564 districts that

may receive CFAP funds and not the 43 districts that may receive

1990 Lookback program funds or the six districts that may receive

Urban program funds. Fig. 1 a depicts the geographic location of

districts receiving CFAP funding from 1997 through 2011. 12 We ob-

serve that over this time period, almost half of non-urban, non-

1990 Lookback school districts have received funding through the

CFAP. Given that the projects are targeted to the least wealthy dis-

tricts first, it is not surprising that many of the wealthy suburban

on historical enrollment figures, open enrollment policies, and projections of new

developments in the area. Given the facility assessment and the enrollment pro-

jections, a Master Facilities Plan is created. This document details the scope of the

work to be completed and the budget for each district’s facilities. The scope of work

includes enrollment projections, grade configurations of the facilities, cost figures

for renovation or replacement, and whether any additions to existing structures are

needed. The OSFC will often recommend replacing a building if the cost of renovat-

ing the existing structure exceeds two-thirds the cost of building a new facility. The

budget for each facility is determined using the total allowable gross square footage

(a function of the school enrollment) multiplied by a cost per square footage mea-

sure (a function of school size and the grade configuration of the facility). 9 While the program restricts the type of expenditures that the CFAP subsidizes,

local districts can augment the CFAP facility plan with a ”locally funded initiative”

which is funded solely by local district revenue. These initiatives are used to fund

expenditures on extra gymnasium space, extra classrooms, athletic facilities and au-

ditoriums. 10 Urban program projects are much larger and feature disproportionately more

school building consolidations than CFAP projects. In fact, the average cost of a

project is approximately $550 million for those in the Urban Program, compared

to the average cost of slightly over $40 million for those in the CFAP, and the state

subsidy is slightly over half of the total cost. 11 While the 1990 Lookback program is very similar in terms of structure to the

CFAP, most notably in how districts are selected for eligibility based on district

property wealth, there are differences. The Lookback districts have a slightly dif-

ferent state subsidy formula and there are restrictions on how many Lookback dis-

tricts can be funded in a given year. While likely due in part to differences in ex-

isting capital facilities, eligible Lookback districts are less likely to participate in the

subsidy program than those districts eligible for CFAP funding and often delay par-

ticipation several years after becoming eligible. On average, participation by the 24

Lookback districts occurred 4.33 years after they were first eligible for funding and

participation by the 231 CFAP districts occurred 0.35 years after they were first eli-

gible for funding. 12 The year used in this paper refers to the school year, which runs from July to

June. Thus, our data span the 1996–1997 to 2010–2011 school years.

Fig. 1. Geographic and yearly distribution of CFAP subsidies.

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istricts outside of the major urban areas of Cincinnati, Cleveland,

nd Columbus have not yet received CFAP funding.

Fig. 1 b depicts the total projected costs of all funded projects

eceiving a CFAP subsidy (left axis) and the proportion of the to-

al cost of the capital project that is paid for by the state funds

right axis) from 1997 to 2011. The number listed below each year

n the figure denotes the number of districts that took up the

FAP in a given year. Fig. 1 b indicates that total project costs and

he total number of districts receiving CFAP funding has varied

idely over the time frame, with the large spikes observed in 1999

nd 20 07–20 08 coinciding with two influxes in funding. The 1999

pike, in which 38 districts undertook construction projects with

otal costs of $1.17 billion, coincided with the introduction of the

Rebuild Ohio” plan

13 and the spike in 20 07–20 08, in which 52

istricts received CFAP funds, coincided with the securitization of

hio’s share of the Tobacco Settlement as an immediate cash pay-

ut. Since 2010, however, the number of projects per year has been

uite small. This reduction in the number of projects coincides, not

nly with the exhaustion of the tobacco funds, but also with the

ecession and stress on the state budget.

13 This policy, implemented by then-governor Taft, called for $23 billion dollars

n state and local funding to assure that safe and adequate school facilities were

resent in every school district.

M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16 5

Fig. 2. CFAP participation decisions by eligible districts, by year.

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16 Districts commonly issue debt through a bond referendum to pay for the local

share, with property taxes collected to pay off the principal and interest over a 25

to 30 year term. In addition, school districts receiving OSFC funding must raise a

Because districts with lower property wealth are eligible ear-

ier and the state share is dependent on the district’s rank per-

entile, the general decrease in state share across years, as depicted

n Fig. 1 b, is expected. The main reason the state share in Fig. 1 b

ncreases across certain years is that not all districts choose to or

re able to participate the first year they become eligible for the

FAP. Some districts decide that, even with the state subsidy, the

ocal share of the construction and renovation costs stipulated in

he OSFC’s Master plan is too costly. Fig. 2 identifies the rank cut-

ffs and the number of eligible districts who choose to participate

nd not to participate each year. For example, the figure indicates

hat the rank cutoff in the first year of the CFAP was 25 and 16 el-

gible districts participated while one eligible district did not par-

icipate. 14 Fig. 2 also distinguishes between districts that partici-

ated the first year of eligibility and those that delayed participa-

ion. For example, of the 38 districts that began participating in

999, 34 first became eligible in 1999 while the other four dis-

ricts were eligible in 1997 and/or 1998. There were also six dis-

ricts that became eligible in 1999 and chose not to participate

mmediately.

Fig. 2 illustrates several aspects of the program that are im-

ortant when evaluating our identification strategy. First, the cut-

ff changes dramatically across years which is attributable to the

olatility of the funding. 15 Second, most districts participate im-

ediately after becoming eligible and, for those that delay, most

articipate within a year or two of eligibility. Of the 231 districts

hat have participated between 1997 and 2011, 191 did so the year

hey became eligible and 20 did so a year after becoming eligible.

hird, of the 323 districts that were eligible since the inception of

he program in 1997, 92 have not participated as of 2011. Most of

hese districts first became eligible post-2007. While not depicted

n Fig. 2 , there is no clear within year pattern when comparing

14 The remaining eight districts ranked between 1 and 25 were either Urban pro-

ram or 1990 Lookback program districts, which are not included in our analysis. 15 Interestingly, the cutoff decreases from 360 to 312 from 2009 to 2010 and the

011 cutoff is still significantly less than the 2009 cutoff. The large influx of funding

n 20 07–20 08 resulted in many eligible districts choosing not to initially participate

n the CFAP. When CFAP funding decreased in 2010 and 2011, the OSFC was unable

o offer funding to all eligible districts in 2009 (specifically those with rankings

reater than 324 and less than 360).

0

r

e

d

t

m

s

m

he ranking of those districts that participated in their first year of

ligibility to the ranking of those who did not participate immedi-

tely.

A district’s decision to not participate or delay participation is

ften based on an inability to secure the local share of the project

unding. Districts can obtain the local share through cash reserves

r contributions from outside sources, but often raise the funds

hrough passage of a referendum involving a new property tax

illage. 16 Based on school district referendum elections involving

ocal funds for CFAP projects from 2006 to 2010, the majority of

istricts over this period raised local funds by passing a tax refer-

ndum. While 96 districts participated in the CFAP over this five

ear period, voters approved 62 referenda that provided funding

or the local share. While 62 referenda passed, there were 75 ref-

renda over this five year period pertaining to the local share of

FAP projects that failed. Given that referenda can be voted on

ultiple times a year, many school districts will place a previously

efeated referendum back on the ballot in the hope that the ref-

rendum will pass on a different uniform election date. 17 In terms

f referenda involving the local share of CFAP projects, there are

any cases where the district, after having voters defeat the initial

eferendum, placed another referendum on a subsequent election

hich the voters passed. There are also a few cases where a refer-

ndum failed to pass but the district obtained the local share from

n alternative source. 18

.5 mill permanent improvement tax for the continued maintenance of the new or

enovated facilities. 17 Kogan, Lavertu, and Peskowitz (2017) document this for Ohio school district ref-

renda and find that the probability of a district proposing and passing a referen-

um is greater if the district had a referendum fail in the prior year. They also find

hat this probability of proposing and passing a referendum depends on by what

argin the prior referendum failed. 18 A few districts with existing permanent improvement taxes or other revenue

ources chose to earmark some of this revenue to satisfy the local share require-

ent.

6 M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16

Table 1

School district characteristics: means and standard deviations.

School Enrollment 2562 Operating Expend PP 8725

(2342) (2870)

Taxable Value PP 122,105 Capital Tax Millage Rate 3.81

(160,929) (2.76)

Median Income 60,296 Capital Prop Tax Rev PP 500

(16,453) (465)

Fraction of School 0.12 Operating Prop Tax Rev PP 3970

Age Children in Poverty (0.07) (3073)

Fraction Free or 0.24 Housing Sale Price 155,778

Reduced Lunch (0.17) (129,969)

Fraction Black 0.05 % Proficient in Math 75.60

(0.13) (11.46)

Capital Stock PP 8793 % Proficient in Reading 82.48

(8296) (8.15)

Capital Expenditures PP 1305

(2636) School Districts 564

The expenditure and revenue per-pupil variables are listed in 2013 dollars. Standard de-

viations are listed in parentheses.

A

m

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i

an actual value of net capital assets was recorded. The results are very similar to

4. Data

The CFAP data used in the descriptive statistics in the previ-

ous section were collected from the OSFC and include the yearly

equity list ranking and cutoffs from 1997 to 2011, the year of el-

igibility and, if applicable, the year the project started, the year

the project was completed, the total projected project cost, the

local share of the project, and details on the master plan (e.g.,

number of buildings demolished, built, renovated). The tax referen-

dum election results come from the Ohio Secretary of State’s office

and include information on the type of referendum (e.g., renewal,

replacement, additional), its purpose (e.g., whether bond was for

CFAP local share or for non-CFAP related capital project), the dura-

tion of the measure, and the proposed millage rate. While we have

election results from 2004 to 2012, we can only identify CFAP re-

lated projects for 2006–2010. We augment these data sets with in-

formation on school district finances, tax rates, taxable values, test

scores, and residential home sales.

Detailed information on expenditures and revenues is obtained

from the National Center for Education Statistics (NCES) Com-

mon Core of Data. In addition to information on total capital ex-

penditures, this data set provides disaggregated information on

the amount spent on school district infrastructure, including the

specific amounts spent on new construction, instructional equip-

ment, land and existing structures. We also collect data from the

Ohio Department of Taxation on all tax rates levied by school

districts, which include the specific purpose of each tax and the

yearly tax rate. These tax data are supplemented with yearly tax-

able values for real property, tangible personal property, and tan-

gible public utility property for each school district. Combining

these two data sets allows us to calculate total tax revenue and

tax revenue earmarked for capital expenditures. From yearly au-

dit reports for these districts, we also obtain data on the dol-

lar value of the capital stock. This is measured as the total dol-

lar value of the capital housed within the district and accounts

for depreciation. 19 Yearly student test score proficiency levels in

19 Information on total capital assets (i.e., total expenditures on the capital stock)

is available from 1997 to 2011, but information on net capital assets (i.e., total cap-

ital assets net depreciation) and the amount of total depreciation is only available

from 2003 to 2011 for a majority of districts. We use the changes in total capital as-

sets between years from 1997 to 2002 and the average depreciation rate from 2003

to 2011 for each district to impute the missing values for net capital assets in the

prior years. However, this has little bearing on our analytic sample, as the use of 6

or 7 lags of the rankings and cutoffs in our 2SLS analysis creates a situation where

we are only estimating effects based on the 2002–2011 or 2003–2011 time periods.

s a robustness check we do estimate our analyses only using district-years where

s

p

n

c

t

m

d

s

ath and reading for all tested grades are collected from the

hio Department of Education. School district demographic data

re obtained from the NCES Common Core of Data and the Small

rea Income and Poverty Estimates. These data include the num-

er of free and reduced priced lunch students, enrollments bro-

en down by race, and the number of school-aged children in

overty.

Finally, housing sales data are collected from individual Ohio

ounty auditors. 20 These data include the location of the prop-

rty, the date of sale, the sale price, numerous characteristics of

he home, and the tax district in which the property resides. 21 The

ata from these various sources are linked to each parcel using

he associated tax district. To ease the constraints in collecting the

ousing transaction data, the sample is restricted to include only

ingle-family homes with sale prices that exceed $10,0 0 0. The an-

lytic sample contains 915,456 parcel sales with all the relevant

arcel characteristics.

Table 1 provides summary statistics for the 564 Ohio districts

hat are neither Urban nor 1990 Lookback districts between 1997

nd 2011. 22 This table contains means and standard deviations of

nnual enrollment, tax base, demographic characteristics, capital

nd operating tax revenues and expenditures, capital stocks, home

ale prices and student test scores. It is interesting to note that the

verage capital stock per pupil is six times as large as the average

apital expenditure per pupil and there is large variation across

istricts in both. In terms of home prices and student test score

roficiency, the average home price is $155,778 and the percent

roficient is slightly greater in reading than in math (82.48% com-

ared to 75.60%). Also of interest is the fact that the variation in

ath proficiency is significantly larger than the variation in read-

ng proficiency.

the baseline results presented in the text and are available upon request. 20 The 58 counties included in the analytic sample (for the housing price regres-

ions) are quite representative of the entire state. The districts in the analytic sam-

le have slightly larger enrollments and spend slightly more per student, but are

early identical on all of the other covariates of interest to the state as a whole (88

ounties). 21 A tax district corresponds to a unique county-township-city-school combina-

ion. Within a school district there may be a number of different tax districts. This

eans that the taxes faced by residents within a given school district may vary

epending on which tax district the resident resides. 22 For a full description of the relevant variables and data sources used in this

tudy see Appendix Table A.1 .

M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16 7

5

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F

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–

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Fig. 3. Probability and intensity of treatment.

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a

b

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i

i

t

a

. Relationship between district eligibility and district

haracteristics

To estimate the dynamic effect of capital expenditures on stu-

ent test scores and home prices, we use variation in CFAP eligi-

ility. Before using this variation to estimate our instrumental vari-

ble specification, we first provide a descriptive analysis of how

rogram participation, student test scores, home prices and school

istrict characteristics (such as student body composition, district

xpenditures and tax revenue/rates) vary with the difference be-

ween a district’s equity ranking and the yearly cutoff. Districts

hat rank below the equity cutoff (i.e., the poorer districts) are eli-

ible to receive the CFAP funding while those above the cutoff (i.e.,

he wealthier districts) are not eligible. Given the dynamic nature

f the cutoff, how far a district’s ranking is below the cutoff pro-

ides a proxy for the number of years the district has been eli-

ible and is related to the district’s capital expenditures. It is an

mprecise measure of capital expenditures because only some eli-

ible districts chose to participate, some districts delay participa-

ion, CFAP construction projects can range between 2 and 6 years,

nd some projects only begin after a delay.

We present this descriptive analysis for three main reasons.

irst, the relationship between a district’s rank and a district’s

apital investment decisions (as quantified by capital expenditures

nd the value of the capital stock) illustrates the variation used

or identification in our instrumental variable specification. Second,

he fact that how far an eligible district’s rank is below the cut-

ff is a proxy for capital expenditures provides suggestive evidence

n the dynamic effect of capital expenditures on student perfor-

ance and home prices. Third, presenting a descriptive analysis on

hanges in the composition of the student body and district oper-

ting expenditures provides insight into the mechanisms through

hich the subsidized capital expenditures can influence student

est score performance and home prices.

For ranks between 200 above and 200 below the cutoff,

ig. 3 depicts how this difference between district rank and cutoff

where the difference is negative for eligible districts and positive

or ineligible districts) relates to program participation and years

f eligibility. 23 Fig. 3 a indicates that the probability of CFAP par-

icipation is nearly 50 percentage points higher for districts just

elow the cutoff than those just above the cutoff and participation

ncreases for lower ranked districts – those farther away from the

utoff. As observed in Fig. 3 b, eligible districts with lower rank-

ngs have greater exposure to the program with an approximate

0 rank decrease associated with an additional year of treatment

xposure. 24

Fig. 4 depicts school district capital stock per pupil, expendi-

ures per pupil, millage rates and tax revenues per pupil for dis-

ricts rankings between 200 below and 200 above the cutoff. At a

inimum, CFAP eligibility should be heavily impacting capital ex-

enditures as the program funded a billion dollars of construction

nnually to assess, update, and rebuild the depleted capital stock

n these districts. This analysis provides a visual depiction of the

23 The scatterplots in Figs. 3 through 6 depict the bin averages and second order

olynomials for eligible and non-eligible districts. Since the rankings change each

ear and the cutoff rises dynamically over the time span, we normalize the cut-

ff to zero in each year. Given that we have a panel data set, doing this leads to

situation in which the same district can lie on either side of the cutoff in dif-

erent years. While this is problematic for conducting a causal analysis using this

pproach, it does allow us to conduct the descriptive analysis we are interested in

to examine how capital expenditures, dollar value of the capital stock, test scores,

ousing prices, and other district outcomes vary based on district rank relative to

he eligibility cutoff. 24 Fig. 3 b also depicts some districts above the cutoff having been exposed to the

rogram. This is due to the cutoff decreasing from 2009 to 2011 and changes in

istricts’ equity list rankings across years.

i

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o

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c

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elationship between our instrumental variable (variation in tim-

ng of eligibility) and the endogenous variables of interest (capital

xpenditures and capital stock), which is the variation used to ob-

ain the first stage estimates from our two-stage least squares re-

ression described in the next section. Fig. 4 a indicates that while

he value of the capital stock per pupil does not appreciably vary

ith ranking for districts that are not eligible nor between districts

round the eligibility cutoff, there is a substantial increase as eligi-

le districts rank farther away from the cutoff. This indicates that

he program substantially increases the value of the capital stock

n eligible districts as the construction projects proceed. The graph

n Fig. 4 a suggests that a one year increase ( ∼30 change in rank) in

he number of years since first becoming eligible is associated with

n approximate $2,0 0 0 per pupil increase in the value of the exist-

ng capital stock. This rise in value of the capital stock is due to the

ignificant increase in capital expenditures undertaken by districts

fter becoming eligible for the CFAP. Fig. 4 b indicates that capi-

al expenditures rise substantially in the first few years of district

ligibility and then begin to decrease at approximately a ranking

f 100 below the cutoff. This decrease occurs as the construction

rojects are becoming finalized and new buildings are becoming

ccupied and, given Fig. 3 b, corresponds to around the third year

f eligibility. Given that district residents must approve property

axes funding the local share of the project once the district be-

omes eligible, we do observe a noticeable discontinuity at the el-

gibility cutoff in both the millage rate for capital ( Fig. 4 c) and tax

evenue per pupil collected for capital expenditures ( Fig. 4 d). After

8 M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16

Fig. 4. Capital stock, expenditures and taxes, by CFAP eligibility rank.

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d

d

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p

25 In terms of the relationship of district taxable value per pupil and median in-

come with the difference between district rank and eligibility cutoff, we find that

both increase relatively monotonically with district rank and there are negligible

discontinuities at the eligibility cutoff. Given that we are examining a ranking based

on these wealth measures, both of which are largely out of districts’ control, these

results are expected. In addition, due to uncertainty surrounding how many projects

will be funded each year, it is hard for districts to strategically time capital invest-

becoming eligible for the program, the average district approves a

tax increase of nearly 1.75 mills, which leads to an average increase

in tax revenue for capital expenditures of about $135 per pupil.

To provide insight into the welfare effects of the CFAP, Fig. 5

examines the relationship between the district’s ranking relative to

the cutoff and housing prices or test scores. This analysis provides

a visual depiction of the variation underlying the reduced form es-

timates from the two-stage least squares regression described in

the next section. Home prices do not appear to differ significantly

for those districts ranked just below compared to just above the

cutoff and do not appear to depend significantly on how an eligible

district’s rank compares to the eligibility cutoff. Based on the rela-

tionship in Fig. 4 between capital expenditures and how district

rank compares to the eligibility cutoff, this is somewhat surprising

since home prices are affected by both current and (expectations

of) future capital expenditures and capital expenditures associated

with the CFAP are heavily subsidized by the state (especially for

the lower ranked districts).

While there does not appear to be a significant relationship be-

tween CFAP eligibility and housing prices, the relationship between

eligibility, as well as duration of eligibility, and the percent of stu-

dents proficient in math and reading is interesting. While there is

a linear relationship for districts that are not eligible and the per-

centages are only slightly greater for districts just below the eli-

gibility cutoff relative to those just above the cutoff, the most in-

teresting aspect is the non-linear relationship for eligible districts.

For eligible districts ranked within approximately 80 of the cutoff,

the percentages of students who are testing as proficient in math

and reading decrease as their ranking decreases. Based on Fig. 4 b,

these reductions in test score proficiency coincide with the height

of construction in these districts. For rankings that are more than

80 below the cutoff, which are those districts where construction

subsides and projects are more likely completed, the percentage of

m

tudents meeting these test score thresholds substantially rises for

ath and reading as ranking decreases.

The graphs in Figs. 4 and 5 suggest that the effect of CFAP con-

truction projects on district-level standardized test performance

ay depend on whether construction is ongoing or completed. To

rovide insight into the mechanisms through which these effects

ay occur, we consider how students and districts may respond

o CFAP eligibility. While we do not believe districts can effectively

anipulate their ranking to become eligible for CFAP, 25 we con-

ider whether program eligibility affects student enrollment deci-

ions and district operating expenditures.

Student enrollment decisions may be affected by a district’s de-

ision to participate in the CFAP and likely depend on how disrup-

ive the ongoing capital projects are to the learning environment

nd on expectations of the future quality of the learning environ-

ent once the construction projects are completed. To provide in-

ight on whether these capital investments affect student enroll-

ent decisions, Fig. 6 considers how district enrollment and the

omposition of the student body vary with the difference between

istrict rank and eligibility cutoff. Fig. 6 a indicates only a minimal

iscontinuity in enrollment at the eligibility cutoff and a minimal

ifference in how enrollment varies with rank for eligible and in-

ligible districts. However, Figs. 6 b–6d do provide some evidence

hat the composition of the student body may vary with program

articipation. While the changes in the fractions of school age chil-

ent decisions prior to becoming eligible.

M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16 9

Fig. 5. Test scores and housing prices, by CFAP eligibility rank.

d

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ren in poverty and on free-reduced lunch vary with rank in a

imilar manner for eligible and non-eligible districts, fraction black

ariation with rank depends on eligibility. Specifically, the fraction

f students who are black does not vary appreciably with rank for

on-eligible districts but increases significantly with a decrease in

ank for eligible districts. Fig. 6 d also indicates that the fraction

f the student body that is black is appreciably less for districts

hose rank is just below the cutoff compared to just above the

utoff.

As for district operating expenditures, Fig. 6 e indicates that el-

gible districts spend more per-pupil on operating expenditures,

s their length of eligibility increases. Given that there is no dis-

ernible discontinuity in operating property tax revenues per pupil

Fig. 6 f), it could be the case that the greater per pupil operating

xpenditures are attributable to poorer districts (i.e. lower rank)

eceiving more federal funds. It may also be the case that the sub-

idized capital expenditures are allowing districts to direct more

esources from the general fund to operating expenditures.

The results in Fig. 6 provide insight on possible reasons for the

on-linear relationship in Figs. 5 b and 5 c between district-level

tudent test scores and rank for those districts eligible for CFAP

onstruction funds. One possible explanation for this non-linear re-

ationship is that while ongoing construction projects may be a

isruption to the learning environment, the capital investments,

pon completion, do promote student learning through avenues

uch as updated technology in the classroom and allowing more

esources to be allocated to operating expenditures. 26 Another pos-

26 Jackson, Johnson, and Persico (2016) find that exogenous spending increases

y school districts increased student educational attainment and resulted in higher

arnings. They also document how these spending increases were associated with

eductions in student-to-teacher ratios, increases in teacher salaries, and longer

a

i

s

l

ible explanation is that changes in student composition vary dif-

erentially in response to ongoing construction projects and com-

leted construction projects.

. Estimating the returns to school district infrastructure

nvestments

Since distance from the cutoff is an imprecise measure of pro-

ram participation and capital expenditures, we directly test the

ynamic effects of capital expenditures on home prices and test

cores using an instrumental variables approach. We conjecture

hat district capital stock may influence test scores and home

rices and, due to the possibility that construction may disturb the

earning environment, the effect may depend on when the capi-

al expenditures occurred. Therefore, we model test scores as lin-

ar functions of not only the capital expenditures in the current

ear but also the district’s capital expenditures in prior years. To

ddress endogeneity issues associated with districts’ current and

agged capital expenditures, we instrument using CFAP eligibility.

In terms of modeling student test scores, we first assume that

istrict proficiency rates can be represented by:

dt =

∞ ∑

y =0

βy CE d,t−y + βr rank dt + βX X dt + εt + εd + εdt (1)

here A dt is district d ’s percentage of students who were deemed

t least proficient on the state standardized math or reading exam

n year t ; CE d,t−y is district d ’s capital expenditures in year t − y ;

chool years. These results suggest that increases in operating expenditures could

ead to better long term student outcomes.

10 M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16

Fig. 6. Student body characterististics and operating expenditures/taxes, by CFAP eligibility rank.

a

f

y

β

a

rank dt is the district’s current year CFAP rank; 27 X dt is a vector of

time varying district-level characteristics; εt is a random variable

that captures unobserved factors in year t that influence exam per-

formance but do not vary across districts; εd is a random variable

that captures unobserved district-level factors that influence exam

performance but do not vary across years; and εdt is a random

variable that captures unobserved district-level factors that vary

across years and influence test performance.

27 Current year rank is included as a covariate because a district’s tax base and

median income could directly influence student test scores. The estimates obtained

from our two-stage least squares estimation do not change appreciably if we ex-

clude district rank from the set of covariates in the second stage. Results of this

sensitivity analysis are available upon request.

w

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e

To address the unobserved district-level factors that do not vary

cross years, we first difference the data. Assuming that the dif-

erential effect of prior capital expenditures in two consecutive

ears is constant for lags greater than two years (i.e., βy − βy −1 =K , ∀ y > 2 ), the change in the fraction of district d students who

re proficient in math or reading can be written as:

A dt − A d,t−1 = β0 CE dt + (β1 − β0 ) CE d,t−1 + (β2 − β1 ) CE d,t−2

+ βK CS d,t−3 + βr (rank dt − rank d,t−1 ) + βX (X dt − X d,t−1 )

+ (εt − εt−1 ) + (εdt − εd,t−1 ) (2)

here CS d,t−3 is district d ’s capital stock three years prior

i.e., ∑ ∞

y =3 CE d,t−y net depreciation).

When estimating the above specification, our primary endo-

eneity concern is the inability to control for the residents’ prefer-

nces for education which are likely to affect both capital expendi-

M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16 11

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ures and test scores. Therefore, we instrument for capital expen-

itures in the current and prior two years, as well as for the value

f the capital stock three years prior, using districts’ CFAP equity

anking and CFAP cutoff in current and prior years. Whether the

istrict is currently eligible and whether the district was eligible

n prior years are strong predictors of capital expenditures and the

alue of the capital stock based on Fig. 4 a and b. These graphs also

uggest that the effect of program eligibility on capital expendi-

ures varies depending on how long ago the district first became

ligible.

We use these instruments to estimate the system of equations

n (3), (4), and (5) using two-stage least squares:

E ds = αs +

3 ∑

y =0

μsy 1[ rank d,s −y ≤ 0] +

3 ∑

y =0

φsy f (rank d,s −y )

+ βX �X ds + εds , for s=t to t-2 (3)

S d,t−3 = αt−3 +

6 ∑

y =3

μy 1[ rank d,t−y ≤ 0] +

6 ∑

y =3

φy f (rank d,t−y )

+ βX �X d,t−3 + εd,t−3 (4)

A dt = αt + β0 CE dt + (β1 − β0 ) CE d,t−1 + (β2 − β1 ) CE d,t−2

+ βK CS d,t−3 + βr �rank dt + βX �X dt + εm

(5)

here αs , αt−3 and αt are year fixed effects ( αt = εt − εt−1 );

[ rank d,s −y ≤ 0] is an indicator variable for whether district d ’s

ank in year s − y is below that year’s rank cutoff chosen for

FAP eligibility; f (rank d,s −y ) is a third-order polynomial in the

ank; 28 �A dt is the change in the math or reading proficiency

ate for district d in year t ( A dt − A d,t−1 ); �rank dt is the change

n CFAP ranking, for district d in year t ( rank dt − rank d,t−1 ); �X dt

s the change in observable characteristics for district d in year t

X dt − X d,t−1 ); and εds , εd,t−3 and εm

are idiosyncratic error terms

εm

= εdt − εd,t−1 ).

We begin by estimating Eq. (5) using two-stage least squares

2SLS). In the first stage, we regress each of the endogenous vari-

bles, CE t , C E t−1 , C E t−2 , and CS t−3 , on the rankings and cutoffs

rom year t through t-6 and the controls from equation (5) to ob-

ain the fitted values. 29 In the second stage, we regress the first

28 This includes a third-order polynomial of not only the rank on its own but also

third-order polynomial of the rank interacted with the yearly rank cutoff. We as-

ess the sensitivity of the results to the choice of polynomial by also estimating

pecifications of the baseline model using either a second-, fourth- or fifth-order

olynomial in rank in place of the third-order polynomial. We also include an ad-

itional sensitivity analysis where we do not include a flexible polynomial in rank,

hereby using only the eligibility cutoffs as instruments. The main conclusions are

nchanged when using these sensitivity analyses suggesting that the results are

uite robust to the choice of polynomial. These results are available upon request. 29 Although the specifications given in the system of Eqs. (3) –(5), indicate that

ach endogenous variable is instrumented by its own set of instruments, the com-

on approach is to use all instruments, along with the second-stage regressors, in

ach first stage regression. Even with irrelevant or redundant instrumental variables

or some of these endogenous variables (e.g., cutoff in year t used as an instrument

or capital expenditures in t -2), this approach still leads to consistent estimates of

he causal effect of capital expenditures and capital stock on achievement and hous-

ng prices and provides asymptotically valid standard errors (see Wooldridge, 2010).

nd, in fact, the results in Table 2 indicate, as expected, that cutoffs from years af-

er the capital expenditures are incurred have no significant effects on previous year

apital expenditures. However, to better reflect the exclusion restrictions we outline

n equations (3) and (4) , we implement two alternative empirical specifications. The

rst approach is to use 2SLS, but only include as instruments the rankings and cut-

ffs that are associated with all of the endogenous variables defined in equations

3) and (4) . Thus, in the case of the specification that includes concurrent and two

ags of capital expenditures and a three-year lag of capital stock, we use only the

ankings and cutoffs from years t -3 to t -6 as instruments. The second approach is

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ifferenced proficiency rates or housing prices on the fitted val-

es from the first stage and the controls listed in equation (5) . We

lso estimate specifications with one additional lag. This amounts

o conducting the same 2SLS analysis on a specification that in-

ludes CE t , CE t−1 , CE t−2 , CE t−3 , and CS t−4 as the endogenous vari-

bles in equation (5) and uses the rankings and cutoffs from year

through t -7 as instruments. 30

The results of the first-stage regressions for per pupil capital

xpenditures and capital stock are presented in Table 2 . Regardless

f the set of endogenous variables and instruments we use, the re-

ults demonstrate that being eligible (i.e., below the CFAP rank cut-

ff) two or three years earlier has a large effect on capital expen-

itures in these districts. Being eligible two years prior is associ-

ted with an increase in capital expenditures of between $464 and

1,085 per pupil and being eligible three years prior is associated

ith an increase in capital expenditures of between $2,143 and

3,317 per pupil. However, being eligible five years earlier is as-

ociated with a decrease in capital expenditures of between $3,104

nd $4,129 per pupil, which is primarily due to construction taper-

ng off in districts that participated in the CFAP program soon after

rst becoming eligible. The first stage estimates for the value of the

apital stock per pupil are listed in columns (8) and (9) of Table 2 .

e find that being eligible three years prior is associated with an

ncrease in the value of the capital stock of between $4,710 and

5,905 per pupil. These estimates indicate that these lagged eligi-

ility cutoffs are strong predictors of capital expenditures and the

alue of the capital stock and are consistent with the results pre-

ented in Figs. 4 a and b.

The estimates from the second-stage are presented in Columns

1) and (2) of Table 3 with the first column including two lags of

apital expenditures and the second column including three lags

f capital expenditures. It is important to note that the coefficients

ssociated with the lagged capital expenditures and capital stock

n equation (5) are estimates of βy − βy −1 while the coefficient as-

ociated with concurrent capital expenditures is an estimate of β0 .

owever, for clarity, we use these results to calculate estimates of

0 , β1 , β2 and β3 which are reported in Table 3 . 31 For comparison

ith the two-stage least squares results, we also estimate equa-

ion (5) using ordinary least squares (OLS). These OLS results, in

olumns (3) and (4) of Table 3 , generally find an attenuated effect

elative to the 2SLS results.

For math achievement (Panel A), the timing of the capital ex-

enditures appears to matter. While concurrent capital expendi-

ures are positively associated with math achievement, capital ex-

enditures from the recent prior years are associated with reduc-

ions in math test score performance. For example, the estimates

n column (1) indicate that a $10 0 0 per pupil increase in capital

xpenditures in the prior year leads to a decrease of 0.145 percent-

three-step estimator that is conducted as follows: (a) separately regress each of

he endogenous variables defined in equations (3) and (4) on the specified set of

ankings and cutoffs and equation (5) controls; (b) obtain fitted values for each

f the endogenous variables; (c) use 2SLS with the fitted values used as instru-

ents for the endogenous variables. This technique has been successfully applied

n previous studies, including Adams, Almeida, and Ferreira (2009) and Mitchell,

ochran, Mears, and Bales (2016) . When using either the 2SLS estimator with the

ubset of instruments or the three-step estimator, we find similar results to those

n Table 3 and these estimates are available from the authors upon request. 30 Given that we only observe the rankings and cutoffs back to 1997 (i.e., the start

f the program), including lags of the rankings and cutoffs as far back as t -6 or t -7

s instruments restricts the sample size considerably. Thus, we conduct the same

SLS analysis for a specification that includes CE t , CE t−1 , and CS t−2 as the endoge-

ous variables in equation (5) and uses the rankings and cutoffs from year t through

-5 as instruments. The results of this analysis are available upon request. 31 We obtain these coefficients and the associated standard errors by using the

incom command in STATA. We use the following linear combinations to back out

he estimates of β1 , β2 and β3 : (a) ˆ β1 =

ˆ β0 +

(β1 − β0 ) ; (b) ˆ β2 =

ˆ β0 +

(β1 − β0 ) +

β2 − β1 ) ; (c) ˆ β3 =

ˆ β0 +

(β1 − β0 ) +

(β2 − β1 ) +

(β3 − β2 ) .

12 M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16

Table 2

First stage results.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Capital Capital Capital Capital Capital Capital Capital Capital Capital

Exp PP t Exp PP t Exp PP t−1 Exp PP t−1 Exp PP t−2 Exp PP t−2 Exp PP t−3 Stock PP t−3 Stock PP t−4

1[ rank dt ≤ 0] −0.356 −0.290 −0.022 0.216 0.023 0.147 0.101 0.662 0.497

(0.304) (0.334) (0.312) (0.322) (0.251) (0.283) (0.258) (0.501) (0.540)

1[ rank d,t−1 ≤ 0] 0.348 0.408 −0.146 −0.170 −0.0 0 0 0.032 −0.073 0.393 0.256

(0.373) (0.379) (0.276) (0.289) (0.309) (0.337) (0.260) (0.491) (0.470)

1[ rank d,t−2 ≤ 0] 0.552 ∗ 0.464 0.166 0.112 −0.128 −0.201 −0.047 0.858 0.742

(0.318) (0.319) (0.292) (0.325) (0.264) (0.305) (0.347) (0.545) (0.585)

1[ rank d,t−3 ≤ 0] 2.335 ∗∗∗ 2.143 ∗∗∗ 0.993 ∗∗∗ 0.944 ∗∗∗ 0.020 0.065 −0.213 −0.514 0.234

(0.520) (0.560) (0.302) (0.344) (0.348) (0.350) (0.347) (0.635) (0.624)

1[ rank d,t−4 ≤ 0] 0.857 0.765 2.986 ∗∗∗ 3.006 ∗∗∗ 0.704 1.085 ∗∗ −0.171 −0.604 −0.049

(0.599) (0.659) (0.613) (0.652) (0.436) (0.457) (0.471) (0.686) (0.796)

1[ rank d,t−5 ≤ 0] −3.245 ∗∗∗ −3.104 ∗∗∗ 0.560 0.810 3.082 ∗∗∗ 3.081 ∗∗∗ 0.975 ∗ 1.783 ∗ −0.160

(0.478) (0.504) (0.646) (0.684) (0.684) (0.703) (0.536) (0.963) (0.822)

1[ rank d,t−6 ≤ 0] −1.488 ∗∗∗ −1.269 ∗∗∗ −3.676 ∗∗∗ −3.300 ∗∗∗ 0.820 1.055 3.317 ∗∗∗ 4.710 ∗∗∗ 0.865

(0.435) (0.455) (0.593) (0.560) (0.758) (0.714) (0.789) (1.153) (0.985)

1[ rank d,t−7 ≤ 0] −0.699 ∗∗ −1.772 ∗∗∗ −4.129 ∗∗∗ 0.617 5.905 ∗∗∗

(0.352) (0.461) (0.670) (0.864) (1.196)

S-W F-statistic 5.90 5.21 7.51 5.40 7.66 7.56 7.38 12.32 11.71

Observations 4 4 47 3958 4 4 47 3958 4 4 47 3958 3958 4 4 47 3958

R-squared 0.214 0.202 0.262 0.234 0.261 0.266 0.255 0.387 0.394

Capital expenditure and capital stock variables are listed in $10 0 0s. Robust standard errors, clustered at the school district level given in paren-

theses. The S-W F-statistic gives the Sanderson–Windmeijer multivariate F test of excluded instruments for each first-stage equation. We also

compute the weak identification tests for the whole system using the Cragg–Donald (C–D) and Kleibergen–Paap (K–P) Wald F-statistics. For the

2SLS regression with CE t , CE t−1 , CE t−2 , and CS t−3 as included endogenous variables the C–D Wald F-statistic is 7.45 and the K–P Wald F-statistic

is 4.86. For the 2SLS regression with CE t , CE t−1 , CE t−2 , CE t−3 , and CS t−4 as included endogenous variables the C-D Wald F-statistic is 3.72 and the

K–P Wald F-statistic is 3.04. ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1.

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age points in the percent of students testing at least proficient in

math. When examining the case with three lags of capital expen-

ditures included (column 2), we find that these decreases in math

achievement persist for several years after the capital expenditures

are incurred. The estimates in Panel B suggest that the effects of

recent capital expenditures on reading proficiency levels are also

negative, but the point estimates are generally not as large as those

found for math and are statistically insignificant. This difference in

the size of the effect of capital expenditures on math and reading

performance may be driven by the fact that school inputs often

more directly impact math test scores, while reading performance

is often dictated by household inputs (“learn math at school and

reading at home”). 32

The negative relationship between recent capital expenditures

and reductions in math test score performance could be a result of

disruptions caused by ongoing construction (e.g., noise, displace-

ment from school buildings). However, the impact of these disrup-

tions on test scores may not be immediate. The fact that current

32 Since the percentage of students scoring proficient or above on the math or

reading tests is a crude measure of test score performance, we test the sensitivity

of these results using other measures of achievement and performance as depen-

dent variables in the OLS and 2SLS regression specifications. We first use a perfor-

mance index that measures student performance in each district across all subjects

on the Ohio Achievement Assessments at the 3rd to 8th grade levels and the Ohio

Graduation Test in 10th grade. Secondly, we use an index of the math and reading

teacher value added scores in these districts. We standardize these indices to have

a zero mean and standard deviation of 1, which allows us to interpret the results in

standard deviations of achievement or value added. While the results are available

upon request, we largely draw similar conclusions to the baseline case when using

these indices as dependent variables. Most notably, we find that a $1,0 0 0 per pupil

increase in the value of the capital stock is associated with an increase in achieve-

ment of 0.02 standard deviations. These results are slightly smaller than the 0.05

to 0.1 standard deviation increases found by Kogan, Lavertu, and Peskowitz (2017) ,

but they examined operating expenditure increases in conjunction with increases

in capital expenditures. The teacher value added results tell a similar story as the

achievement results – with a $1,0 0 0 per pupil increase in the value of the capital

tock associated with an increase in math and reading teacher value added of 0.013

and 0.012 standard deviations, respectively.

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ear expenditures have a small positive effect, while previous year

xpenditures have a negative effect, suggests that either the school

istrict incurs some expenses prior to construction commencing,

uch of the current construction occurs after the students have

aken the exams, or there is a lag in terms of the construction

roject disruptions showing up in test scores. In addition to dis-

uptions during the construction, some disruptions may still exist

ven after buildings become occupied. As many of these projects

ave a school building consolidation component, students may be

ransferred into a new school building and/or be integrated into

building with new grade configurations (e.g., from a traditional

iddle school to a combined middle and high school) once con-

truction is complete. Previous literature on student displacement

ue to school closings ( Brummet, 2014; Engberg, Gill, Zamarro, &

immer, 2012; Kirshner, Gaertner, & Pozzoboni, 2010; Sacerdote,

012; de la Torre & Gwynne, 2009 ) has suggested that these type

f school building transitions have negative effects on student test

core performance. Our results here suggest that transitions into

nd out of school buildings due to ongoing construction and build-

ng consolidations due to large-scale capital investments may have

imilar negative effects.

The short-term negative effects associated with the disruption

rom construction and district reconfiguration may be followed

y long-term positive benefits from cleaner, quieter, healthier and

ore productive learning environments. To examine the long-term

mpact of these capital investments more explicitly, we examine

he effect of increasing the value of the capital stock in the dis-

rict on math and reading proficiency. The coefficient estimate on

he capital stock variable in the specification that uses first differ-

nces measures how the timing of capital expenditures differen-

ially affects test score gains ( βy − βy −1 = βK , ∀ y > 2 ) . These esti-

ates, however, do not allow us to infer how increasing the qual-

ty of the capital stock affects achievement levels. Thus, we instead

se 2SLS to estimate specifications that do not first difference

quation (1) ; thereby having the specifications estimate levels and

ot gains. The estimates associated with capital stock in Columns

M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16 13

Table 3

Instrumental variables results.

(1) (2) (3) (4) (5) (6)

2SLS 2SLS OLS OLS 2SLS 2SLS

gains gains gains gains levels levels

Panel A: Percent Proficient in Math

Capital Exp PP t [ β0 ] 0.096 0.081 0.0 0 0 0.002 0.547 ∗∗∗ 0.591 ∗∗∗

(0.097) (0.124) (0.030) (0.031) (0.161) (0.170)

Capital Exp PP t-1 [ β1 ] −0.145 ∗∗ −0.082 −0.067 ∗∗ −0.048 0.018 −0.184

(0.066) (0.120) (0.030) (0.032) (0.143) (0.184)

Capital Exp PP t-2 [ β2 ] 0.001 −0.044 −0.007 −0.035 −0.125 0.335 ∗

(0.055) (0.105) (0.029) (0.040) (0.136) (0.180)

Capital Exp PP t-3 [ β3 ] −0.127 ∗ 0.002 −0.312 ∗∗

(0.066) (0.030) (0.142)

Capital Stock PP t-3 [ βstock ] 0.057 ∗∗∗ 0.025 ∗∗∗ 0.139 ∗∗

(0.012) (0.009) (0.060)

Capital Stock PP t-4 [ βstock ] 0.046 ∗∗∗ 0.001 0.164 ∗∗∗

(0.015) (0.008) (0.062)

Observations 4 4 47 3958 4 4 47 3958 4 4 47 3958

R-squared 0.052 0.048 0.060 0.057 0.691 0.681

Panel B: Percent Proficient in Reading

Capital Exp PP t [ β0 ] −0.001 0.070 −0.036 ∗ −0.014 0.282 ∗∗ 0.217 ∗

(0.069) (0.064) (0.021) (0.021) (0.119) (0.119)

Capital Exp PP t-1 [ β1 ] −0.050 −0.064 −0.010 −0.015 0.029 0.074

(0.049) (0.068) (0.023) (0.023) (0.111) (0.135)

Capital Exp PP t-2 [ β2 ] 0.013 −0.003 0.001 −0.029 0.046 0.117

(0.044) (0.068) (0.018) (0.029) (0.099) (0.133)

Capital Exp PP t-3 [ β3 ] 0.049 0.036 ∗ 0.003

(0.043) (0.020) (0.102)

Capital Stock PP t-3 [ βstock ] 0.013 0.010 0.104 ∗∗

(0.008) (0.007) (0.042)

Capital Stock PP t-4 [ βstock ] 0.009 0.002 0.103 ∗∗

(0.009) (0.005) (0.046)

Observations 4 4 46 3957 4 4 46 3957 4 4 46 3957

R-squared 0.048 0.052 0.051 0.060 0.681 0.690

Panel C: Housing Prices

Capital Exp PP t [ β0 ] −0.998 ∗ −1.290 ∗∗ −0.039 −0.111 0.522 −0.082

(0.589) (0.653) (0.162) (0.181) (0.822) (1.068)

Capital Exp PP t-1 [ β1 ] −0.846 ∗∗ −0.386 −0.563 ∗∗∗ −0.591 ∗∗∗ 0.993 1.459

(0.373) (0.527) (0.160) (0.177) (0.825) (1.265)

Capital Exp PP t-2 [ β2 ] −0.156 −0.701 0.017 0.063 −0.551 0.411

(0.355) (0.470) (0.197) (0.290) (0.747) (1.008)

Capital Exp PP t-3 [ β3 ] 0.485 0.044 −0.174

(0.404) (0.220) (0.784)

Capital Stock PP t-3 [ βstock ] −0.125 ∗ −0.041 0.797 ∗∗

(0.074) (0.048) (0.358)

Capital Stock PP t-4 [ βstock ] −0.211 ∗∗∗ −0.031 0.788 ∗∗

(0.080) (0.055) (0.374)

Observations 2225 1834 2225 1834 2226 1835

R-squared 0.171 0.156 0.179 0.179 0.791 0.796

Capital expenditure and capital stock variables in Panels A and B are listed in $10 0 0s. Proficiency rate de-

pendent variables are scaled from 0 to 100. Each specification includes a set of district-level characteristics

(including population, the percentage of children living in poverty, enrollment, percentage of students in

free/reduced lunch program, percentage of black students, and taxable value per pupil). The gains specifica-

tion estimates equation (5) in the second stage, while the levels specification uses equation (1) in the second

stage. Robust standard errors, clustered at the school district level are given in parentheses. ∗∗∗ p < 0.01, ∗∗

p < 0.05, ∗ p < 0.1

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33 We may expect these effects to be largest in schools with a small number of

school buildings, as more students are directly impacted by the construction, than

in a district with larger numbers of school buildings where construction may be

spread out over a longer period of time. To directly test for this potential hetero-

geneity, we stratify the sample into two groups: small/medium (1–5 school build-

ings) and large (6+ school buildings) districts. We then separately estimate, by

group, the 2SLS regressions using both the first differenced dependent variables

and the levels. Surprisingly, we find similar levels of short term effects of capital

expenditures for both small/medium school districts and large school districts and

5) and (6) of Table 3 , where the second stage estimates equation

1) , suggest that increases in the value of a district’s capital stock

ignificantly increases both math and reading test score proficien-

ies. While these estimates are similar to what other researchers

ave found, we approach them with caution because they are ob-

ained using primarily cross-district variation. That said, the esti-

ates do suggest that the percentage of students testing at least

roficient in math increases between 0.139 and 0.164 percentage

oints and increases by 0.103 percentage points for reading when

he value of the capital stock increases by $10 0 0 per pupil. The re-

ults in Panels A and B of Table 3 suggest that the early learning

osses due to the possible disruptions from construction and re-

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onfiguration are offset by the long-term benefits associated with

he improvement in the quality of the capital stock. 33

arger positive impacts of increasing the value of the capital stock on achievement

14 M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16

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Similar to math and reading exam performance, we test

whether school district capital expenditures have a dynamic effect

on the housing market by estimating specifications using home

sale prices as the dependent variable in equation (5) and including

home characteristics in the set of covariates. The results from these

specifications are contained in Panel C of Table 3 . Similar to test

scores, the first difference estimates in Columns (1) and (2) suggest

that home prices are impacted differently depending on the timing

of the capital expenditures. These 2SLS estimates indicate that con-

current capital expenditures and capital expenditures in the prior

two years decrease home prices. Specifically, a $1 increase in capi-

tal expenditures per pupil in the concurrent year is associated with

between a $1 and $1.29 decrease in housing prices. The negative

effects are smaller in magnitude for capital expenditures incurred

during the prior two years, with a $1 increase in capital expendi-

tures per pupil associated with between a $0.16 and $0.85 decrease

in housing prices. 34 Despite the negative housing price effects of

recent capital expenditures, the long-term impacts of capital in-

vestment on housing prices appear positive. The capital stock esti-

mates in Columns (5) and (6), which are obtained when equation

(1) is not differenced and uses primarily cross-district variation for

identification, suggest that a $1 increase in the value of the cap-

ital stock per pupil is associated with slightly less than an $0.80

increase in housing prices. 35

There are several explanations for why school district capital

expenditures would have short-term negative effects and long-

term positive effects on the housing market. The large negative

effects of current year capital expenditures on home prices could

be caused by home owners paying taxes towards funding these

projects, while the ongoing capital projects provide little to no cur-

rent benefits to taxpayers (as the new or renovated buildings are

unlikely to be fully operational) and may even impose a cost if

construction causes disruptions to student learning. Economic the-

ory would suggest that families should take expectations of fu-

ture tax burdens and future benefits of these new schools when

making housing decisions. But due to imperfect information and

uncertainty as to how these schools will look once completed,

when they will be completed and how tax burdens will change

over time, many homebuyers may have difficulty in accounting for

the long-term costs and benefits while the construction projects

are underway. In addition, if families with children are easily able

to delay their home purchases until after the school construction

project is completed, then we would expect to see negative hous-

ing price effects during construction and positive effects when con-

struction is close to completion. This would explain not only the

relatively large negative effects we observe for concurrent expen-

in large school districts. Specifically, we find that a $1,0 0 0 per pupil increase in the

value of the capital stock is associated with an increase of around 0.26 percentage

points in math for large school districts and between a 0.084 and 0.119 percentage

point increase for small school districts. The full set of results are available upon

request. 34 The OLS estimates in Columns (3) and (4) also suggest that recent capital ex-

penditures decrease home prices. 35 There are interesting differences in the housing price impacts when looking

across the number of school buildings in the district. Perhaps most interesting is

the fact that, although the dynamic effects on student test performance do not dif-

fer based on the size of the school district, the negative short-term effects of capital

expenditures on home prices are greater for smaller school districts. This may be at-

tributable to the CFAP program often resulting in construction projects at all schools

in the smaller districts and at only a subset of schools in the larger districts. In ad-

ition, the overall construction at the smaller districts are not as extensive and are

often completed in a shorter time period than for larger districts. However, when

looking at long-term housing price effects from increases in the capital stock, hous-

ing prices in smaller school districts appear to increase more than those in larger

school districts. In particular, a $1 increase in the value of the capital stock per pupil

is associated with around a $0.78 increase in housing prices in small school districts

and between a $0.26 and $0.64 increase in large school districts (albeit statistically

insignificant). These results are also available upon request.

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itures but also the smaller negative effects for capital expendi-

ures in years t − 1 and t − 2 , and the positive effect for capital

xpenditures in year t − 3 . Once the projects are completed, the

ositive benefits of the new capital stock (e.g., increased academic

erformance; cleaner, quieter, and healthier learning environment)

re fully realized and the disruption costs of construction are no

onger present. These changes, along with the fact that much of the

apital expenditure is significantly subsidized by the state, likely

ontribute to a case where the benefits of the revitalized capital

tock outweigh the tax burden facing the residents. Thus, it is not

urprising to find that housing prices are positively impacted by

nvestment in capital stock in the long run.

. Conclusion

This paper analyzes a statewide capital subsidy program for

chool districts in Ohio to estimate the dynamic effect of capital

xpenditures on student test scores and housing prices. Using pro-

ram eligibility for identification in an instrumental variables de-

ign, we generally find that a $10 0 0 per pupil increase in capi-

al expenditures in the prior two years leads to less than a 0.15

ercentage point decrease in the percentage of students testing

t least proficient. This short-term decline is consistent with the

remise that construction projects are disrupting student learning,

ue to construction noise, displacement of students while build-

ngs are renovated, or issues with building consolidations. We find

hat a $10 0 0 per pupil increase in the value of the capital stock,

ur measure of building quality, leads to a slightly greater than

.1 percentage point increase in the percent of students testing at

east proficient in math or reading. This suggests that test scores

mprove once construction on the new and renovated buildings

s completed. In terms of the housing market, our estimates also

uggest a short-term negative effect and long-term positive effect

f school district capital expenditures. We find that a $1 increase

n concurrent year capital expenditures per pupil decrease home

rices by between $1.00 and $1.29 and decrease between $0.16 and

0.85 when the expenditures occur in the prior two years. In re-

ards to the long-term, improvements in the quality of the capi-

al stock increase home prices with a $1 increase in the value of

he capital stock being associated with an approximately $0.80 in-

rease in housing prices. This could be due to homeowners imme-

iately paying taxes towards funding these construction projects

hich are disruptive in the short-term but beneficial in the long-

erm. These dynamic effects on home sale prices are also influ-

nced by the fact that many of these school construction projects

re subsidized by the state.

As noted earlier, our paper complements much of the recent

uasi-experimental work examining the effects of school district

apital investments on test scores and housing prices. Comparing

ur test performance estimates to existing research, we find that

ur achievement results are generally smaller or similar-sized to

hat has been found in other studies. Goncalves (2015) estimates

slightly larger construction disruption effect, finding that math

cores fall by 2.2 percentage points after four years of exposure

o construction, but finds no statistically significant long-term pos-

tive impacts of the OSFC capital projects. Hong and Zimmer ex-

mine reading proficiency in Michigan, but find similarly sized ef-

ects to our math results in the first four years after bond proposal,

ut find slightly larger effects in later years (between 0.4 and 0.7

ercentage point increases in reading proficiency). Although not

irectly comparable to results of studies looking at standard de-

iation changes in test scores, our long-term results amount to

round a 0.01 to 0.02 standard deviation increase in proficiency

ates in math. These standard deviation changes are on the smaller

M. Conlin, P.N. Thompson / Economics of Education Review 0 0 0 (2017) 1–16 15

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ide, relative to studies, such as Cellini et al. (2010) , which finds

0.077 standard deviation increase in math scores and a 0.067

tandard deviation increase in reading scores. Comparing the hous-

ng market results to the prior literature, our estimates are slightly

maller than what has been found in other studies. Cellini et al.

2010) , find that a $1 increase in dollars of the bond issued is

ssociated with a rise in housing prices of between $1.39 and

1.79. Our housing price result suggests that a $10,0 0 0 increase in

rior year capital expenditures is associated with a 1.2% increase

n housing prices relative to the average housing price in our sam-

le, which is similar in size to the 10% increase found in Neilson

nd Zimmerman (2014) as a result of $70,0 0 0 per pupil increase in

apital expenditures.

The results of our paper and the others in this literature sug-

est that despite the potential for negative effects during the con-

truction phase, investments in school infrastructure yield modest

ong-term positive impacts on academic achievement and are val-

ed positively by the housing market. These results suggest build-

ng aid programs may be effective at enhancing overall achieve-

ent levels in some districts, through both directly increasing cap-

tal expenditures and allowing for greater general funds to be used

n operating expenditures. As our results can only speak to im-

acts in relatively low wealth districts, additional work is needed

n other institutional settings to determine how these types of sub-

idy programs may be successful at promoting capital investment

n various types of school districts. While our paper uses a cred-

ble instrumental variable identification strategy to estimate the

ynamic effects of school district capital expenditures on student

est performance, it is similar to some of the existing literature on

chool district capital expenditures in that it uses panel district-

evel data instead of student-level information. Thus, we are unable

o ascertain how these upgrades to capital expenditures impact the

chievement of different student sub-populations (e.g., low-income

tudents, minority students). Student-level information would also

llow for student mobility into districts with higher quality cap-

tal stock to be better assessed. This type of analysis may be es-

ecially relevant in states like Ohio with an open enrollment pol-

cy. 36 Determining which types of students are most impacted

y the disruptions caused by these projects and which students

enefit the most from a high-quality capital stock will help pol-

cymakers and school districts better implement these types of

rojects.

36 Ohio allows students to attend a school district even if they do not reside in

hat district and the state provides the attending district additional funds to educate

hese students. The school superintendent determines which students are allowed

o enroll in the school district when the number of open enrollment applications

xceeds the number of slots available. School districts in Ohio also have the option

o limit what students are eligible to enroll in the district through open enrollment.

urrently, 425 school districts allow any student from the state to apply for open

nrollment. Of the remaining districts, 91 only accept students from adjacent dis-

ricts and 148 have no open enrollment policy in place.

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ppendix A

able A.1

ariable names and definitions.

Variable name Description

CFAP Characteristics a

CFAP Equity List Ranking Yearly equity list ranking

Cutoff Yearly equity list cutoff for eligibility (eligible if

rank < cutoff)

School District Financial

Characteristics b

Capital Expenditures per

Pupil

Total capital expenditures divided by

enrollment

Capital Stock per Pupil Dollar value of capital assets in the district net

depreciation divided by enrollment

Capital Tax Millage Rate Total effective millage rate levied on class I

property to fund capital expenditures

Capital Property Tax

Revenues per Pupil

Total local property tax revenue for capital

expenditures divided by enrollment

Operating Expenditures per

Pupil

Total operating expenditures divided by

enrollment

Operating Property Tax

Revenues per Pupil

Total local property tax revenue for operating

expenditures divided by enrollment

School District

Demographics c

Total Enrollment Total district enrollment

Taxable Value per Pupil Total taxable value of property in the district

divided by enrollment

Median Income Median income of the district in 2010 $

Fraction Free/Reduced Lunch Fraction of students that are eligible for free

and reduced price lunch

Fraction Black Fraction of students that are black

School-Aged Children in

Poverty

Fraction of school age children residing in the

district that live in poverty

Percent Proficient in Math Percentage of students scoring proficient or

better on math achievement test

Percent Proficient in Reading Percentage of students scoring proficient or

better on reading achievement test

Parcel Characteristics d

Sale Price The sale price of the house

Rooms The total number of rooms in the house

Bedrooms The total number of bedrooms in the house

Total Baths The total number of bathrooms in the house

(half bath = 0.5)

Living Area The total square footage of the living area in

the house

Year Home Built The year when house was originally

constructed

a Source: Data from Ohio School Facilities Commission. b Source: Data from NCES Common Core of Data and individual Ohio Audit re-

orts. c Sources: Data from NCES Common Core of Data and the Small Area Income and

overty Estimates. d Sources: Data from county auditor websites in 68 of 88 counties in Ohio (58

ounties have all housing characteristics).

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